Thursday, 24 November 2016

Media coverage from recent paper: Predicting Smartphone Operating System from Personality and Individual Differences

Shaw Heather, Ellis David A., Kendrick Libby-Rae, Ziegler Fenja, and Wiseman Richard. Cyberpsychology, Behavior, and Social Networking. December 2016, 19(12): 727-732. doi: 10.1089/cyber.2016.0324.
I am no longer going try and compile complete lists for media coverage, but this work has appeared in The Daily Mail (UK), The Mirror (UK), The Metro (UK), Slate (France), ESS (Finland), MK (Korea), Aftenposten (Norway), The New York Post (USA), The Inquirer (USA), CNET (USA), and Vogue (USA).

Monday, 3 October 2016

Wearable manufacturers are still not letting customers view their raw data

I've worn a Garmin wearable fitness tracker religiously for the last 9 months. The device has now become unreliable, but with so much data collected, I was curious to quantify patterns based on physical activity and sleep (this model measures both).

However, while I can view a daily step count using the online service Garmin Connect, I also wanted to download my total step count for each day and run my own separate analysis.

But this isn't possible (see below).

I own the device, but not the data. Garmin can provide access to an API, but this remains expensive according to this reddit feed. It would actually be cheaper to build my own device and use that instead.

Personally, I don't see how preventing customers from accessing their own raw data can continue. I understand why a manufacture would restrict access to the exact algorithm that takes accelerometer data and converts this data into steps, but my request outlined above is entirely reasonable. Manufacturers could even run competitions where people are encouraged to develop new predictive analytics/insights from this data, which could be integrated into new products and services.

The attitude of consumer wearable manufacturers here is totally at odds with other areas of the tech industry. For example, both Facebook and Twitter provide access to user generated data on request. However, as colleagues and I discussed earlier this year, manufacturers remain uninterested or unwilling to allow users access to their own data from wearable devices.

Wednesday, 3 August 2016

Rosenberg self-esteem scale: SPSS Script

The Rosenberg self-esteem scale is a psychological inventory based on a 4-point likert scale and consists of 10 questions. It is used extensively to measure self-esteem across the social sciences.

Below is a short script for SPSS which will help speed up the coding process. 

All items should be labeled as separate numeric variables as R1, R2...etc

The script computes and prints the results for all reverse scored items and then calculates the total score

*Part 1 - reverse scoring of specific items

COMPUTE R3 = 5 - Q3.
COMPUTE R5 = 5 - Q5.
COMPUTE R8 = 5 - Q8.
COMPUTE R9 = 5 - Q9.
COMPUTE R10 = 5 - Q10.

*Part 2 - total score

COMPUTE Rosenberg = Q1+Q2+R3+Q4+R5+Q6+Q7+R8+R9+R10.


  /VARIABLES=Q1 Q2 R3 Q4 R5 Q6 Q7 R8 R9 R10

Friday, 22 July 2016

Open science reading list

Science has its problems, but many early career researchers (myself included) can often struggle when it comes to knowing how we can improve systems that we still very much have to operate within on a daily basis.

That said, I am a firm believer that making research readily available to others is something that we should all work towards where possible. This applies to publications, data, computer code/software and the peer review process.

The references below are taken from my own reading, but this list certainly isn't exhaustive.

All of these papers pull in the same direction. Specifically, they provide convincing evidence that open access research practices help science as well as the individual researcher.

Early career researchers, who are typically gifted very little time to get ideas off the ground and demonstrate that they have societal importance, will help their own cause by ensuring that work is readily available across multiple disciplines and beyond.

Moving forward, the next major issue for open access is no  longer whether it should be at the centre of the mainstream scholarly communication system, but how it will work effectively. 

Antelman, K. (2004). Do open-access articles have a greater research impact?. College & research libraries65(5), 372-382.

Davis, P. M. (2011). Open access, readership, citations: a randomized controlled trial of scientific journal publishing. The FASEB Journal25(7), 2129-2134.

Donovan, J. M., Watson, C. A., & Osborne, C. (2014). The open access advantage for American law reviews. Edison: Law+ Technology (JPTOS's Open Access Journal), Forthcoming.

Harnad, S., & Brody, T. (2004). Comparing the impact of open access (OA) vs. non-OA articles in the same journals. D-lib Magazine10(6).

Kousha, K., & Abdoli, M. (2010). The citation impact of Open Access agricultural research: A comparison between OA and non-OA publications.Online Information Review34(5), 772-785.

Lawrence, P. A. (2008). Lost in publication: how measurement harms science. Ethics in science and environmental politics8(1), 9-11.

PLoS Medicine Editors. (2006). The impact factor game. PLoS Med3(6), e291.

Piwowar, H. A., & Vision, T. J. (2013). Data reuse and the open data citation advantage. PeerJ1, e175.

Sandve, G. K., Nekrutenko, A., Taylor, J., & Hovig, E. (2013). Ten simple rules for reproducible computational research. PLoS Comput Biol9(10), e1003285.

Siebert, S., Machesky, L. M., & Insall, R. H. (2015). Overflow in science and its implications for trust. Elife4, e10825.

Walsh, E., Rooney, M., Appleby, L., & Wilkinson, G. (2000). Open peer review: a randomised controlled trial. The British Journal of Psychiatry,176(1), 47-51. 

Wang, X., Liu, C., Mao, W., & Fang, Z. (2015). The open access advantage considering citation, article usage and social media attention. scientometrics,103(2), 555-564.

Wicherts, J. M. (2016). Peer review quality and transparency of the peer-review process in open access and subscription journals. PloS one11(1), e0147913.

Tuesday, 5 July 2016

The effect of exam anxiety on time perception

The brief study reported below appeared in an earlier draft of my PhD, but wasn't included in the final version. The sample is small, but the results are interesting enough to merit a blog entry. If nothing else the method certainly hints towards the abundant use of smartphones in future work!


States of mind characterised by disturbed time perception (e.g. schizophrenia [Davalos et al. 2003] and some hallucinogenic states [Wittman et al. 2007]) can severely disrupt normal functioning. However, even in normal mental states, time perception is surprisingly labile. For example, during threatening events such as car accidents and robberies, many people report that events seem to pass in slow motion - as if time has slowed down. Emotions in a more general sense are also known to influence and sometimes distort time perception. Droit-Volet and Meck (2007) for example, observed that the exposure of emotional faces were overestimated compared to neutral ones.

The attentional gate model might explain why people are relatively inaccurate in making timing judgements during emotional experiences (Figure 1). In some cases, emotions demand attentional resources for coping with those events and duration estimates will be underestimated. In other cases, especially when emotions have a threatening quality, the duration will be overestimated. However, researchers have often struggled to examine this within a typical population because simulating a state of anxiety is both difficult and not ethically palatable. In this experiment, we bypassed this issue this by conducting a short experimental task outside an exam hall, where people are naturally anxious (Arana and Furlan 2016). We therefore predicted that anxiety directly before an exam would positively correlate with estimates that underestimate actual duration.

Figure 1. A pacemaker produces a series of pulses that are fed into an accumulator. The number of pulses that has been recorded for a given time span represents experienced duration. The pulse number is compared with stored representations of time periods that can be verbalized (as seconds or minutes). Attentional gate models  assume that only when attention is directed to time pulses are accumulated. 


Written consent was obtained from participants in advance of their exam, but all participants remained naive to the purpose of the study until they met with the experimenter outside the exam hall. Prior to entering an undergraduate exam hall at The University of Glasgow, participants were required to use an iPhone App (Figure 2) and adjust the rate of flashing to what they best perceived to be a rate of one flash per second (1Hz). They were also asked to give brief measures of anxiety and relief on a simple 1 to 10 scale. This procedure was repeated after they left the exam. Their exam performance was also recorded.

Figure 2. Screenshot from an iPhone app that allowed participants to adjust the rate of flashing to best match a rate of 1Hz.

13 first year psychology students studying at the University of Glasgow agreed to take part. They received one course credit in return for their participation.


Manipulation check
A paired sample t-test [t(11) = -3.05, = .011] revealed that self-reported anxiety scores were significantly higher before (= 5.67 SD = 2.10) than after the exam (M  = 2.83 SD = 2.29).  No significant changes were observed between time estimates before (M = 67.08 SD = 27.76) or after (M = 62.67 SD = 11.56) the exam [t(11) = -.521, p = .612]

Figure 3. Scatter plot illustrating a positive relationship between self-reported anxiety and adjusted rate of flashing. Red line indicates the correct response (1Hz).

A positive correlation (Figure 3) was observed between self-reported levels of anxiety and participants adjusted rate of flashing [r(11) = .82, p < .01].  A second correlation coefficients showed no significant relationship between anxiety or rate of adjusted flashing post-exam [r = -.15, ns].


In the first instance, this experiment appears to support previous research that increased levels of anxiety show a strong correlation with underestimating duration. In other words, time appeared to speed up, but only for participants who rated their anxiety above 5 (out of 10). The reverse was true for those who felt relatively relaxed. As expected, higher levels of anxiety associated with a forthcoming exam appear to require attentional resources and the duration of flashes was therefore underestimated (Droit-Volet and Gil 2009). What remains unclear however, is whether participants estimates were guided by the flashes themselves or the gap between each flash. This effect could be partly driven by people who are more anxious in general, but this seems unlikely given that the effect disappeared post-exam where participants were no longer anxious. However, it is not possible to rule out practice effects completely. 

Cognitive-processing mechanisms appear to guide our experience of duration. While the perception of time is strongly linked to our subjective-well being, it also varies considerably depending on specific emotional states (Wittmann and Paulus 2007). The feeling that time passes quickly or slowly can be a fair indicator of both psychological distress resulting from an inability to focus meaningful thoughts, but it may also help focus our attention on important activities in other contexts that require considerable cognitive resources. 


Arana, F. G. and Furlan, L. (2016). Groups of perfectionists, test anxiety, and pre-exam coping in Argentine students. Personality and Individual Differences90, 169-173.

Davalos, D. B., Kisley, M. A. and Ross, R. G. (2003). Effects of interval duration on 
temporal processing in schizophrenia. Brain and Cognition. 52, 295–301.

Droit-Volet, S. and Meck, W. H. (2007). How emotions colour our perception of time. Trends in Cognitive Sciences11(12), 504-513.

Droit-Volet, S. and Gil, S. (2009). The time-emotion paradox. Philos Trans R Soc Lond B Bio Sci. 364, 1943-1953.

Loewenstein, G. F. (1996). "Out of control: Visceral influences on behavior. Organizational Behavior and Human Decision Processes. 65(2), 272-292.

Wittmann, M. and Paulus, M. P. (2007). Decision making, impulsivity and time perception. Trends in Cognitive Sciences. 12(1), 7-12.

Tuesday, 24 May 2016

ESRC PhD Studentship: Enhancing our Understanding of Digital Traces


This is now advertised on, but if you wish to discuss the studentship informally or if you have any questions about the position, please contact me (

Monday, 11 April 2016

The failure of folk psychology within psychology

Frequently in psychology, many will proclaim that they could have provided an answer before you ran the experiment. This reminds me of people who walk into an art gallery or listen to a piece of music before announcing that they could have created exactly the same thing or something superiour.

Of course, after taking a few more seconds to think about any piece of art, most of us would admit that even if it was possible to recreate something very similar, we probably wouldn't know why we were engaged with the activity without thinking very deeply about the subject and the underlying message. That said, if the goal of art is to elicit a response, I suppose 'I could do that' does in itself satisfy that condition!

But even in science, psychology appears to elicit this response more than say, theoretical physics.

There are many reasons, not least that a psychology paper is far more likely to reach the mainstream media than one published in theoretical physics. Even without this, the fact remains that everyone, in some capacity, is an amateur psychologist. We can't help but try to understand ourselves and other people.

While this shouldn't come as a huge shock to the system, I remain surprised that many professional psychologists and other scientists fall into a similar pattern of behaviour and will often try to suggest that work is 'intrinsically obvious' or limited to 'folk psychology'.

The term folk psychology doesn't align itself to a scientific perspective, particularly when it comes to making conclusions about people. The average person will only know about a dozen people well (see Dunbar's number) and a few hundred in extreme cases. That prevents any form of assumption or analysis, especially when most of us only know a fairly restricted kind of person. For example, many academics know many other academics and talk about academicy type things. In a rather ironic twist of fate, scientists are, by their own admission, rather detached from the folk part of folk psychology.

Moving beyond what happens in one person's head to the influences and associations across an entire population is not possible with folk psychology, but we should also be mindful that folk psychology occupies a unique place. Folk medicine would generally not be accepted by anyone (except maybe homeopaths), but many psychologists (amateur and professional) continue to accept the notion that folk psychology provides an acceptable explanation or reason to dismiss an idea.

It doesn't.

Google Ngram search results. Y-axis shows percentages of words in the corpus 

Friday, 18 March 2016

List of media coverage from recent paper: The Rise of Consumer Health Wearables: Promises and Barriers

Piwek L, Ellis DA, Andrews S, Joinson A (2016) The Rise of Consumer Health Wearables: Promises and Barriers. PLoS Med 13(2): e1001953. doi:10.1371/journal.pmed.1001953

Altmetric Score:

Media Coverage

Media outlet/theme: BBC Radio 4
Media outlet/theme: The Telegraph

Media outlet/theme: BBC Radio Scotland

Media outlet/theme: The Independent 

Media outlet/theme: The Epoch Times

Media outlet/theme: Medical Daily

Media outlet/theme:

Media outlet/theme: Lancaster University

Media outlet/theme: Science Daily

Media outlet/theme: Uncommon Wisdom Daily

Media outlet/theme: Crescent City Strength

Media outlet/theme: The Conversation (related piece)

Media outlet/theme: Medinside (related piece, German)

Media outlet/theme: Lancaster Guardian

Media outlet/theme: Netzpiloten (related piece, German)

Media outlet/theme: Cycling Active

Thursday, 21 January 2016

APA Tables in R

Generating decent tables in R is something I have struggled with for sometime, particularly when these need to follow APA guidelines. SPSS has proved to be a complete nightmare so in the past I've simply build templates in Word and copied the numbers across manually from the R console.

This is both time-consuming and increases the chances of human error.

Fortunately, David Stanley has written a great library that can quickly generate results and place them in APA tables.

One note of caution - your data frame must be complete (i.e. no missing values) and only include variables you want to appear in the table. Subsetting your main data frame beforehand may be required in the first instance. Otherwise, it is plain sailing:

For example, a data-set with six personality factors calculated from the HEXACO personality inventory across several hundred participants might look like this...

To generate an APA correlation table run the following:

##load library


##load data frame

data <- file="data.csv" font="" header="TURE," read.csv="" sep=",">

##Creates a correlation table in APA style with means and standard deviations

apa.cor.table(data, filename = "correlation.doc", table.number = 1,
              show.conf.interval = FALSE, landscape = TRUE)

data - name of data frame
filename - this is optional, but required to save the table as .doc or .rtf to your working directory
table.number - any integer
show.confidence.interval - self explanatory 
landscape - FALSE for portrait 

This generates a word document:

The code can also be subtly modified to produce other tables:

Creates a table of means and standard deviations for a 1-way ANOVA design in APA style

apa.1way.table(iv, dv, data, filename = NA, table.number = NA,
               show.conf.interval = FALSE, landscape = FALSE)

Creates a table of means and standard deviations for a 2-way ANOVA design in APA style

apa.2way.table(iv1, iv2, dv, data, filename = NA, table.number = NA,
               show.conf.interval = FALSE, show.marginal.means = FALSE, landscape = FALSE)

Creates a d-values for all paired comparisons in APA style

apa.d.table(iv, dv, data, filename = NA, table.number = NA,
            show.conf.interval = TRUE, landscape = TRUE)

Creates a regression table in APA style

apa.reg.table(..., filename = NA, table.number = NA)

... - the regression model has to be entered here. 

The full manual is worth consulting as it provides several more advanced examples on page 6.